Does green transition progress matter for the layoff effects of environmental regulation? Evidence from double machine learning

This study aims to investigate the role of green transition stage in the environmental regulation’s layoff effect. Using a double machine learning (DML) framework, we estimate the impact of China’s Low-Carbon City Pilot (LCCP) programme on employment in heavy-polluting firms at different transition stages, while addressing limitations of conventional linear specifications, including omitted-variable bias and the curse of dimensionality. We obtain four main findings. First, the pilot programme significantly increases research and development (R&D) investment, but it is also associated with employment reductions. Second, these employment losses are concentrated among firms whose transition progress is below the industry average. Third, the policy shapes employment through three channels: cost, output, and structural optimization. Firms that lag in the transition face higher production costs, tighter financing constraints, and rising wage bills, which jointly hinder both job retention and technological upgrading. Fourth, the policy effects exhibit substantial heterogeneity across cities, industries, and firm types. Our results suggest that policymakers should balance employment and innovation objectives when steering the transition of heavy-polluting firms, tailor low-carbon policies to regional, sectoral, and firm-level conditions, and expand green finance coverage, including dedicated ‘transition finance’ instruments to meet diverse corporate needs.